Stats

A news scan found the report of a trading strategy for Elmos, ticker symbol ELG.DE. The strategy was tested on Yahoo dividend-adjusted prices with a 5 basis point fee and a 15 basis point liquidity gap, also known as a slip, plus a 45 basis point stress liquidity gap on each side. The strategy used a simple moving average, or SMA, which is a calculation of the average price of a stock over a certain period of time. It also used an average true range, or ATR, which is a measure of how much a stock's price moves on average. The test ran from 2016 to 2022, after a 200-day warm-up period, and was held out from 2023 to 2025, with a separate test in 2026 up to September 16. The results were: a 26.4273% loss in the training period, with a 42.6213% drawdown, or maximum loss, and a 25% win rate over 32 trades; a 44.4505% loss in the holdout period, with a 65.7873% drawdown and a 15.7895% win rate over 19 trades, and a stress test result of -50.4356%; and a 67.3586% gain in 2026, with a 20.1233% drawdown and a 75% win rate over 4 trades. Despite the positive result in 2026, the strategy did not replace the failed holdout period. The strategy had limits, including only selecting one company per day, using Euro prices that are not traded on the foreign exchange market, and having a fixed time period for the trade. The source of the data is https://query1.finance.yahoo.com/v8/finance/chart/ELG.DE?range=10y&interval=1d&events=div%2Csplits and scripts/tmp/supply92104/test.py, tests.json.

A diagnostic strategy review found that the CLI cross-check differs from the costed bracket tests. The CLI was run with a breakout strategy, looking back 20 periods over 1460 days, with a 10% stop percentage and a 30% target percentage, using data from https://runway.adityaperswal.workers.dev/data/stock-bars, which included all 1001 bars. The results showed the following returns, drawdowns, wins, and numbers for the stocks: LFUS had a return of -17.3486, a drawdown of 27.8021, a win of 32.3944, and 71; ATI had a return of 61.7478, a drawdown of 17.9160, a win of 37.2340, and 94; VSH had a return of 74.9844, a drawdown of 20.8508, a win of 45.1613, and 62; UMC had a return of 31.8682, a drawdown of 26.4650, a win of 40.2778, and 72; PXS had a return of -45.1063, a drawdown of 57.1767, a win of 23.8095, and 63; SEI had a return of -16.3227, a drawdown of 33.4083, a win of 30, and 60. The CLI uses the same close and signal exits and does not have an explicit costed chronological holdout, which is not equivalent to the frozen next-open 2ATR/net 3R60 session vector bt policy. The contradiction in SEI results is due to different exit policies and windows, not because one calculation is wrong. The primary adoption decision uses recorded held-apart costed tests, not the CLI's recent pooled return. There is no order from the CLI.

The R92102 SEI trading strategy has a frozen breakout holdout and cost sensitivity. The strategy is based on several premises, including a rule that freezes the same as rule 4039 before extra results. This means that the strategy will close a position if the current close is greater than the prior 20-day close high and the 50-day Simple Moving Average, which is a calculation of the average price of a stock over a certain period of time. The strategy also uses a stop-loss order, which is an order to sell a stock when it falls to a certain price, set at 2 times the prior Average True Range, which is a measure of the volatility of a stock. The strategy has a net target cost of 3 times the base cost and a maximum of 60 sessions. The strategy was trained on data from 2016 to 2022, with the SEI starting in 2017 and its predecessor business before that. The holdout period is from 2023 to 2025, and there is a separate period in 2026. The strategy uses a long-only approach, which means it only buys stocks and does not sell them short, with a fee of 5 basis points and a liquidity cost of 15 basis points on each side, and a stress test of 45 basis points. The strategy exits positions on the same day they are entered, with a gap stop open, which means that if the stock price gaps up or down significantly, the position will be closed. The strategy also uses a window end mark, which is a way of reconciling net trade cash. The strategy has been tested for sensitivity to different parameters, with 18 different combinations of parameters tested, but no attempt has been made to choose the best combination. The base holdout for the SEI has a drawdown of 24.07288852433759% and a win rate of 35%, with 20 trades and a stress test result of 10.042651723169296%. In 2026, the result was -5.903726863718896% with 4 trades. The current breakout is true, but the power-business acquisitions have changed the regime, which refers to the underlying market conditions. The positive mixed-business holdout is not an independent power-rent event test, which means that it is not a reliable indicator of future performance. The strategy is subject to selected-current-universe bias, which means that the stocks selected for the strategy may not be representative of the broader market, and daily proxy versus 15-minute exit limits remain, which refers to the way that the strategy uses daily data to make decisions about when to exit positions. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the target is the payoff hurdle, which is the minimum return required for the strategy to be considered successful, rather than valuation, which refers to the intrinsic value of the stocks. There is no order to buy or sell stocks based on this information.

The automated trading fund manager made some notes about the stock PXS. The manager uses a set of rules to decide when to buy or sell stocks. These rules include looking at the stock's closing price compared to its high price over the last 20 days and its average price over the last 50 days. The manager also looks at how much the stock's price has changed over the last two days and sets a target price based on this. The manager tested these rules using data from 2016 to 2022 and will use data from 2023 to 2025 to see how well the rules work. The manager will also look at how sensitive the rules are to changes in costs. The manager found that the rules did not work well for PXS, with a loss of 52.17631191970964% and a win of 20%. The manager also found that the rules were very sensitive to changes in costs, with a loss of 56.292478422587614% when costs were high. The manager decided not to use these rules to buy PXS because the return on investment was negative. The manager gets stock price data from https://runway.adityaperswal.workers.dev/data/stock-bars.

A news scan found the following trading notes: Premises for the trade are based on note4041, which freezes the same rule as 4039 before extra results. The trade rule is to close when the price is higher than the prior 20-day close high and the 50-day Simple Moving Average, which is a calculation to find the average price of a stock over a certain period of time. The following open, 2 prior Average True Range stop, which is a measure of price volatility, net 3R base cost target, and 60 session maximum are also considered. The training data is from 2016 to 2022, with SEI starting in 2017 and its predecessor business. The holdout period is from 2023 to 2025, and 2026 is separate. The Vectorbt long-only strategy has a fee of 5 basis points and a liquidity cost of 15 basis points on each side, with a stress test of 45 basis points. The entry day exits, gap stop open, stop first if both, and window end mark are all reconciled to net trade cash. The sensitivity analysis of 10, 20, and 40 percent was observed in all 18 cells, but the best option was not chosen. The UMC base holdout had a return of -9.41 percent, a maximum drawdown of 38.02 percent, a win rate of 35.29 percent, and 17 trades, with a stress test result of -18.20 percent and a 2026 return of 60.08 percent with 10 trades. The current breakout is true, but the negative held-apart return rejects this entry rule. The selected current universe bias and daily proxy versus 15-minute exit limits remain. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the target is a payoff hurdle, not a valuation. No order was given.

The R92102 VSH costed breakout strategy is based on certain premises. It uses a fixed note4039 that considers the completed close price in relation to the prior 20-day close high and the 50-day Simple Moving Average (SMA), which is a calculation to find the average price of a stock over a certain period. The strategy enters at the next open, sets a stop loss at 2 times the prior 20-day Average True Range (ATR), which measures price volatility, and targets a net 3R return at the base 20 basis points per side, with a maximum of 60 sessions. The strategy was trained on data from 2016 to 2022, held apart from 2023 to 2025, and separately tested through September 18, 2026. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The Vectorbt long-only strategy targets a portfolio return of 1000, with a 5 basis point fee and 15 basis points of slippage per side, and a stress test of 45 basis points of slippage. The strategy includes entry-day stops, gap stops at the open, and both-hit stops, with the upside target capped and a forced window exit. The portfolio returns are reconciled to compounded net trade cash within 2.5e-15. The strategy was tested with 10, 20, and 40 lookbacks for sensitivity, without choosing the best one. The base held-out return was -61.1856%, with a drawdown of 63.1110% and a win rate of 5.5556% out of 18 trades. The stress test result was -65.1592%, and the 2026 result was 155.5103% out of 7 trades. The strategy preserves positive evidence where present. Currently, there is no entry signal for all three, and no order is placed. The selection uses current names, adjusted Open, High, Low, and Close (OHLC) prices, and daily exits as proxies, without 15-minute execution or a full corporate action audit. The backtest target is the required payoff, not the fair value, and does not borrow recent winner performance as training. All 18 cells were separately observed.

The R92102 ATI costed breakout strategy is based on certain premises. It uses a fixed note4039 that considers the completed close price in relation to the prior 20-day close high and the 50-day Simple Moving Average (SMA), which is a calculation to find the average price of a stock over a certain period. The strategy enters at the next open, with a stop loss set at 2 times the prior 20-day Average True Range (ATR), which measures the volatility of a stock. The target is a net 3 times the risk, with a maximum of 60 sessions. The strategy was trained on data from 2016 to 2022, held apart from 2023 to 2025, and separate data was used from 2026 through September 18. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy uses a long-only approach, with a target portfolio size of $1000, and includes fees of 5 basis points and slippage of 15 basis points per side, as well as a stress test of 45 basis points slippage. The strategy includes entry-day stops, gap stops at open, and both-hit stops, with the upside target capped and a forced window exit. The portfolio returns are compounded and reconciled within a net trade cash difference of 2.5e-15. The strategy was tested with 10, 20, and 40-day lookbacks for sensitivity, without choosing the best one. The base held-out return was 105.2551%, with a drawdown of 34.2886%, a win rate of 42.1053%, and 19 trades. The stress test result was 83.1403%, and the 2026 result was 63.6936%, with 4 trades. The strategy preserves positive evidence where present. Currently, there is no entry signal for all three, and no order is placed. The selection uses current names, adjusted Open, High, Low, and Close (OHLC) prices, and daily exits as proxies, without 15-minute execution or full corporate action audit. The backtest target is the required payoff, not the fair value, and does not borrow recent winner performance as training. All 18 cells are separately observed.

The R92102 LFUS costed breakout strategy is based on certain premises. It uses a fixed note4039 that considers the completed close price in relation to the prior 20 close high and the 50-day Simple Moving Average (SMA), which is a calculation of the average price of a stock over a certain period. The strategy enters at the next open, with a stop loss set at 2 times the prior 20-day Average True Range (ATR), which measures price volatility. The target is a net 3 times the risk at a base of 20 basis points per side, with a maximum of 60 sessions. The strategy was trained on data from 2016 to 2022, held apart from 2023 to 2025, and separate data was used for 2026 up to September 18. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy uses a long-only target percent portfolio of $1000, with a 5 basis point fee and 15 basis points of slippage per side, and a stress test of 45 basis points of slippage. The strategy includes entry-day stops, gap stops at open, and both-hit stop-first, with an upside target capped and a forced window exit. The portfolio returns are reconciled with compounded net trade cash within 2.5e-15. The strategy has a 10, 20, and 40 lookback sensitivity, with no choose-best approach. The base held-out return is 3.8910%, with a drawdown of 26.9686%, a win rate of 30.7692%, and 13 trades. The stress test return is -3.9046%, and the 2026 return is -2.4526%, with 7 trades. The strategy preserves positive evidence where present. Currently, there is no entry signal for all three, and no order is placed. The selection uses current names, adjusted Open, High, Low, and Close (OHLC) prices, and daily exits are proxies, with no 15-minute execution or full corporate action audit. The backtest target is the required payoff, not the fair value, and does not borrow recent winner performance as training. All 18 cells are separately observed.

This is a report on a trading strategy test. The test used daily stock data from 2015, but the actual data started in 2016 for LPG and on April 29, 2024, for BWLP. The rules for the test were set before the results were known: the stock's closing price had to be higher than its closing price 20 days before, and higher than its 50-day simple moving average, which is a calculation of the average price over 50 days. The test entered a trade at the next open price, with a stop loss at 2 times the true average range of the stock's price over 20 days, and a target at 6 times that range. The test also had a time limit of 60 sessions. The test was hypothetical, with a starting capital of $1000, and fees of 5 basis points plus 15 basis points of slippage on each side of the trade, which means the difference between the expected price and the actual price of the trade. The test also accounted for stress, with an additional 45 basis points of slippage. The test looked back at 10, 20, and 40 days to check the sensitivity of the results, but did not choose the best result after seeing the outcome. The test trained on data from 2016 to 2022, held out data from 2023 to 2025, and tested on recent data from 2026 through September 18. BWLP had no training history before the holdout period. The results showed a base holdout return of -18.7795%, a maximum drawdown of 33.2491%, and a win rate of 25.0000%, with 8 trades and 1 target hit. The stressed return was -22.5860%, and the recent return was 114.3538%, with 7 trades. All the results are available in a file called results.json. The test reused historical windows, which is not the same as a prospective out-of-sample test, and did not account for 15-minute execution or corporate actions. The gross return was below the net return after costs, and was not a fair value. There was no order to buy or sell. The positive recent regime was preserved as counter-evidence, and the current entry signal was false.

A news scan found the following text: Premises: SIP/all adjusted daily data https://runway.adityaperswal.workers.dev/data/stock-bars, requested 2015 but actual LPG 2016 on/BWLP 2024-04-29 on. Rule fixed before results: close > prior 20 close maximum and > SMA50, which means a Simple Moving Average of 50 days; next open enter; stop 2 prior true ATR20, target 6 ATR, force 60 sessions; entry day stop/target allowed, gaps through stop at open, both hit stop first, upside gaps capped at target. Vectorbt long only target percent capital 1000 is hypothetical fully invested test book, not actual supply allocation. Fees 5 bp plus 15 bps lippage each side; stress 45 bps lip. 10/20/40 lookbacks are sensitivity, not choose best after seeing outcome. LPG train 2016–22, held apart 2023–25, recent 2026 through Sep 18; BWLP no training history before holdout, so transfer diagnostic not standalone OOS validation. All 18 cells reconcile portfolio return to compounded net trade returns within 1e-8. Base holdout return 22.9143%, DD 41.2651%, win 35.2941%, N 17, targets 5; stress 10.9951%; recent 86.0430%, N 8. All cells in observations and /tmp/supply92008/results.json. Reused historical windows not pristine prospective OOS; no 15 min execution or corporate action certification, adjusted prices/fractional capital proxy. Gross 3R bracket is below 3R net after costs and not fair value; no order. Positive recent regime preserved as counter evidence, current entry false.

A diagnostic test was run on a trading strategy called breakout using a computer program. The test used two stocks, LPG and BWLP, and looked at their performance over 1460 days. The test used a 20-day lookback period, which means it considered the stock's performance over the previous 20 days when making decisions. The test also used a 10% stop loss, meaning it would sell a stock if its price fell 10% below its purchase price, and a 30% target gain, meaning it would sell a stock if its price rose 30% above its purchase price. The data for the test came from a website at https://runway.adityaperswal.workers.dev/data/stock-bars. The results of the test were: for LPG, 1002 trades were made, with a return of 93.998%, a drawdown of 11.3034%, and a win rate of 38.5542%. For BWLP, 600 trades were made, with a return of 23.1213%, a drawdown of 11.0873%, and a win rate of 40.9091%. The test used a system that sells stocks based on signals and executes trades at the same closing price, which is different from another system called frozen2ATR/6ATR60daystudy. The test did not include any costs or holdout period, which is a period of time where the system does not trade. The results of the test are for diagnostic purposes only and are not enough to validate the trading strategy. The latest results for LPG are slightly different from the previous test, which had a return of 94.8027%. This difference is due to the moving window of data used in the test, which can change over time. No trades were made based on this test.

A backtest was run using the command line interface, which is a computer program that uses text commands, to test a trading strategy on the stocks AA, CENX, CSTM, and KALU. The strategy used was called RSI, which stands for Relative Strength Index, a way to measure stock price movements. The test used 2000 days of data from the website https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex. The test assumed that the trading system would make decisions based on the stock prices at the close of each day, and it did not account for any costs or losses from buying and selling stocks. The results of the test were: CSTM had a 77.1203% return, with a 25.2182% drawdown, which is a measure of the biggest loss, and a 68.2927% win rate, with 41 trades; AA had a 7.7708% return, with a 31.5583% drawdown, and 39 trades; CENX had a -37.2666% return, with a 63.7342% drawdown, and 35 trades; and KALU had a 64.8039% return, with a 23.1352% drawdown, and 43 trades. The test found that the strategy could not be validated, or proven to work, because it was using a new and untested method. The test was replaced with a new analysis that took into account the costs of buying and selling stocks, and this new analysis met the requirements of the command line interface, but it is not evidence that the strategy will work in real trading. The differences between the test and real trading are too big to say whether the strategy will make more money than just buying and holding the stocks.

A trading fund manager analyzed the stock KALU using a trend-filtered pullback strategy. The analysis was based on three premises: first, the manager used daily stock prices from 2016 to 2026, with the data split into training, holdout, and recent periods. Second, the manager used a fixed strategy based on the Relative Strength Index (RSI), a measure of stock price movement, and the Simple Moving Average (SMA), a measure of stock price trend. The manager entered trades when the RSI was below 10 and the stock price was above the SMA, and exited trades when the stock price reached a certain target or stop loss. Third, the manager tested the strategy using a simulation tool called vectorbt, which applied a 5 basis point fee and a 15 basis point slip, and reported a stress test result of 45 basis points slip. The results showed a training return of 24.4367%, a holdout return of 5.4265%, and a recent return of 4.9991%. The manager also tested the sensitivity of the strategy to different RSI parameters and recorded 168 observations. The conclusion was to preserve positive results but not to deploy the strategy live, as the historical targets did not validate certain checks and management strategies.

This is a report about a trading strategy called R92006 CSTM. The strategy is based on two main ideas: first, it uses daily stock prices from 2016 to 2026, with some years used for training and others for testing. The prices are adjusted for dividends and splits. Second, it uses a fixed set of rules to decide when to buy or sell stocks, including a measure called the Relative Strength Index, or RSI, which is a way to measure how quickly a stock's price is changing, and a moving average, which is a way to smooth out price changes over time. The strategy also uses a stop-loss, which is a rule to sell a stock if it falls below a certain price, and a target price, which is a price at which to sell a stock for a profit. The strategy was tested on a set of stocks and the results were: a return of 165.7370% during the training period, a drawdown of 36.0061%, and 25 trades. During the testing period, the return was 74.8097%, the drawdown was 18.5252%, and there were 50% winning trades out of 12 trades. The strategy was also stressed with a 45 basis point slip, which means that the strategy was tested with an additional cost of 0.45% per trade, and the return was 62.6657%. Recently, the strategy had a return of -2.0641% with 4 trades. The report concludes that the strategy should be preserved where it is working, but there is no current signal to buy or sell among the 4 stocks being watched, and the strategy should not be deployed live. The report also notes that the strategy is not validated by historical data and should not be used for discretionary trading or for managing risk.

This is a report about an automated trading fund manager's notes. The manager used a system with the following premises: they looked at daily stock prices from 2016 to 2022 for training, 2023 to 2025 for testing, and 2026 up to September 18 for recent data. They started using this system on November 1, 2016. The system uses a fixed rule based on the Relative Strength Index, or RSI, which measures how fast a stock's price is changing, and a Simple Moving Average, or SMA, which is the average price of a stock over a certain time period. The system buys a stock when the RSI is less than 10 and the closing price is above the SMA, and sells when it hits a stop loss or a target price. The system also has a time limit of 60 sessions. The manager tested this system with a 5 basis point fee and a 15 basis point slip, which is the difference between the expected price and the actual price, and a stress test with a 45 basis point slip. The results were: an 85.6062% return during training, a 45.8463% drawdown, which is the biggest drop in value, and 23 trades; a 91.3941% return during testing, a 55.9653% drawdown, 46.154% wins, and 13 trades; and a 77.0326% return during the stress test. Recently, the system had a 2.7092% return with 3 trades. The manager also tested the system with different parameters and compared it to a generic breakout system. The conclusion is to keep the positive results but not to deploy the system live because there is no current signal among the 4 stocks being considered.

The automated trading fund manager made the following premises: the fund used daily stock prices from 2016 to 2022 for training, 2023 to 2025 for holdout, and 2026 up to September 18 for recent data. The fund started trading Alcoa stock on November 1, 2016. The fund used a fixed strategy based on the Relative Strength Index, or RSI, a measure of stock price movement, and the Simple Moving Average, or SMA, a measure of average stock price over time. The fund entered a trade when the RSI was less than 10 and the stock price was above the SMA, and it set a stop loss, a price at which to automatically sell a stock, and a target price, a price at which to automatically sell a stock for profit. The fund allowed same-day exits, or selling a stock on the same day it was bought, and it used a 5 basis point fee, a 15 basis point slip, and a 45 basis point stress slip, all measures of trading costs. The fund's training return was 176.6188%, its maximum drawdown, or loss, was 33.5615%, and it made 17 trades. The fund's holdout return was -22.9149%, its maximum drawdown was 44.5245%, and it made 7 trades. The fund's recent return was -17.1654%, and it made 4 trades. The fund also tested different variations of the RSI and trend gate, a measure of stock price trend, and it compared its results to a generic breakout strategy, a strategy based on stock price breaking out of a certain range. The fund concluded that it should preserve its positive results but it did not currently have a signal to trade, and it would not deploy its strategy live. The fund also noted that its historical targets did not validate its 15-minute checks, discretionary fundamental targets, or +2R management, a measure of risk management.

This is a report on a test of a trading strategy. The test used daily stock prices from 2016 to 2026. The strategy had certain rules: it would buy a stock if its price was higher than its price 20 days ago and its 50-day average price, and it would sell if the price reached a certain target or stopped losing money after a certain time. The test also considered the costs of buying and selling, including a 5 basis point fee and a 15 basis point cost for buying or selling at a bad price. The test found that all 12 combinations of parameters lost money, so there is no reason to use this strategy just because of a shortage of a certain chemical, called acetyl. The test also found that having more cash can exist at the same time as failing to time the stock market correctly. A news scan found the script files used for the test, including test.py, tests.json, and trades.json.

The automated trading fund manager made some premises for the R92004 CE costed chronological breakout and fixed-bracket test. The premises are as follows: the test uses daily bars from 2016 to September 18, 2026, with 2693 bars per name, which can be found at https://runway.adityaperswal.workers.dev/data/stock-bars. The frozen baseline for the test is a prior 20-day close breakout and a 50-day simple moving average, which is a calculation to determine the trend of a stock, with the next session's open, a stop loss of 2 times the 20-day average true range, which is a measure of volatility, and a target that solves for a net 3R at 20 basis points per side, with a time limit of 60 sessions. The test also considers sensitivity to losses of 40 and 60 percent. The test was trained on data from 2017 to 2022, and then held apart from 2023 to 2025, with recent data from 2026. The test already researched historical windows for CE, so it is not a completely new and untouched test. The fees for the test are 5 basis points plus 15 basis points of slippage per side, with a stress test of 50 basis points of slippage and a frozen target. The test uses a long-only strategy, which means it only buys stocks, and it allows for same-day exits. The test also reconciled the portfolio return to the compounded profit and loss divided by the entry value plus entry fees. The conclusion of the test is that all 12 held-apart parameter and cost cells lose, and there is no basis to adopt this entry solely from acetyl scarcity, which refers to a lack of availability of a certain stock. The test also found that larger cash sensitivity can coexist with failed conditional stock timing.

A diagnostic test was run on a trading strategy using the command line interface, or CLI, with the following settings: the symbols used were CE and EMN, the strategy was a breakout strategy, the lookback period was 20 days, the test lasted for 1460 days, the stop loss was set at 8 percent, and the target gain was set at 24 percent. The data for the test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The test found that the built-in signal decay exits were different from the custom fixed bracket rule. The CLI did not have a specific holdout period and its friction, or the costs of trading, were not separately determined. This test was only for diagnostic purposes and its results were not adopted. The full results of the test can be found in the file /tmp/supply92004/cli.json. The test did not find any evidence that the strategy could consistently predict stock shortages.

A diagnostic test was run using a computer program called node to analyze a trading strategy. The test used the backtest.js program to look at the stocks with the tickers CE and EMN, using a strategy called breakout, which is a type of trading strategy that involves buying a stock when its price breaks out of a certain range. The test looked back at 20 days of data and used 1460 days of data to make decisions. It also used a stop loss, which is a rule to sell a stock when it loses a certain percentage of its value, in this case 8 percent, and a target gain, which is a rule to sell a stock when it gains a certain percentage of its value, in this case 24 percent. The data for the test came from a website called https://runway.adityaperswal.workers.dev/data/stock-bars. The test found that the built-in signal decay exits, which are rules to sell a stock when its signal gets weaker, were different from the custom fixed bracket rule, which is a type of rule to buy and sell stocks. The test also found that the command line interface, or CLI, did not have a clear rule for holding onto stocks over time and that the friction, or costs, of trading were not established independently. This test was only for diagnostic purposes and the results were not adopted. The full results of the test can be found in a file called /tmp/supply92004/cli.json. The test did not find any evidence that the trading strategy could consistently make a profit by predicting shortages.

A diagnostic test was run using the command line interface, which is a way to interact with a computer program using text commands. The test used a node, a type of computer program, to run a backtest, which is a simulation of a trading strategy, on the stocks CE, IMOS, and BTDR. The strategy used was a breakout strategy, which means it looks for stocks that are breaking out of a certain price range, and it looked back at 20 days of data and ran the test for 1460 days. The test also used a stop loss, which is a rule to sell a stock if it loses a certain percentage of its value, in this case 8 percent, and a target profit, which is a rule to sell a stock if it gains a certain percentage of its value, in this case 24 percent. The test got its data from a website, https://runway.adityaperswal.workers.dev/data/stock-bars, and used 1002 bars of data, where a bar is a piece of data that shows the price of a stock at a certain point in time. This test also exits if the signal, or the reason to buy or sell a stock, gets weaker. This test is different from a custom test that uses a different rule to buy and sell stocks. The test did not hold out any data to test its accuracy and did not account for costs, so it is only for diagnostic purposes and its results should not be used to make actual investment decisions. The stock BTDR includes data from before it started operating, and a custom test that only looks at data from when the company was operating gives different results. The full results of the test are in a file called cli_test.json.

A diagnostic test was run using the command line interface, which is a way to interact with a computer program using text commands. The test used a node, which is a computer program, to run a backtest, which is a simulation of a trading strategy, on the stocks CE, IMOS, and BTDR. The strategy used was a breakout strategy, which means buying a stock when its price breaks out of a certain range, and it looked back at 20 days of data and ran for 1460 days. The test also used a stop loss, which is a rule to sell a stock when it loses a certain percentage of its value, in this case 8%, and a target profit, which is a rule to sell a stock when it gains a certain percentage of its value, in this case 24%. The test got its data from a website and used 1002 bars of data, which are like buckets of time. This test also exits when a signal, which is like a hint to buy or sell, gets weaker. This test is different from a custom test that only buys and holds stocks for a long time. The test did not set aside any data to check its results and did not consider any costs, it was just a diagnostic test and its results were not used to make any decisions. The stock BTDR includes data from before it started operating, but a custom test that only looks at data from when it started operating gives a more accurate result. The full results of the test are in a file called cli_test.json.

A diagnostic test was run using the command line interface, which is a way to interact with a computer program using text commands. The test used a node, a type of computer program, to run a backtest, which is a simulation of a trading strategy, on the stocks CE, IMOS, and BTDR. The strategy used was a breakout strategy, which means it looks for stocks that are breaking out of a certain price range, and it looked back at 20 days of data. The test ran for 1460 days and used a stop loss, which is a rule to sell a stock when it falls by 8%, and a target profit, which is a rule to sell a stock when it rises by 24%. The test got its data from a website, https://runway.adityaperswal.workers.dev/data/stock-bars, and used 1002 bars of data, which are like rows of data. This test also stopped when the signal, or the reason to buy or sell, got weaker. This test is different from a custom rule that only buys and holds stocks for a long time. The test did not separate the data into training and testing sets, and it did not account for costs. It was just a diagnostic test and not a real trading strategy. The stock BTDR includes data from before it started operating, and a custom test, number 3591, has more accurate results. The full results of the test are in a file called cli_test.json, located in a temporary directory.

This is a report on a trading strategy test. The test used daily stock prices from September 18, 2016, available at https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy had two main rules: it would buy when the closing price was higher than the maximum price of the last 20 days and the 50-day average, and it would sell when the price reached a target or stopped losing money after 60 sessions. The test also considered costs such as fees and slippage, which are costs associated with buying and selling, and stress, which refers to the impact of unexpected events on the strategy. The test results showed a return of 59.0565% from 2016 to 2022 and 7.8856% from 2023 to September 18, 2026, with a maximum loss of 69.1088% and a win rate of 33.3333%. However, when stress was applied, the return became negative at -6.8632%. A news scan found the conclusion that the strategy has a positive baseline but is sensitive to costs, and stress can make it negative. The test was done on historical data and does not represent a current investment recommendation.

This is a report on a trading strategy called R92002 BTDR long-only breakout. The strategy is based on certain premises: it uses daily high, low, open, and close prices from September 18, 2016, available at https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy has rules such as a stop loss set at 2 times the average true range over 20 days, and a target set at 6 times the average true range over 20 days, with a time exit after 60 sessions. The strategy also takes into account opening gaps and daily path ambiguity. There are costs associated with buying and selling, including a 5 basis point fee and a 15 basis point liquidity cost on each side, with an additional stress test of 5 and 50 basis points. The strategy uses a vectorbt model with long-only direction and independently compounded trade returns. The results show a sensitivity test with L15, L20, and L30, and no best-cell selection. The training data is from 2016 to 2022, but it is not available for BTDR, which only started operating on April 14, 2023. The test results from 2023 to September 18, 2026, show a return of -14.1235%, a maximum drawdown of 75.7896%, a win rate of 33.3333%, and 24 trades, with a stress test result of -27.4041%. The 2026 subset shows a return of 7.1773% with 5 trades. The conclusion is that the strategy shows a baseline long-horizon compounded loss, and any positive recent regime should be preserved, but it does not infer a durable edge. This test does not recommend adoption or ordering, and the historical 3R bracket alone is not a defended current valuation.